This thesis proposes to estimate the speed of a moving acoustic source by either linear or non linear processing of the resulting Doppler shift present in a high-frequency pilot tone. The source is an acoustic modem (Hermes) which currently uses moving average to estimate and compensate for Doppler shift. A new auto regressive approach to Doppler estimation (labeled IIR method in the text) promises to give a better estimate. The results for a simulated peak velocity of 2 m/s in the presence of additive noise showed an RMSE of 0.23 m/s using moving average vs. 0.00018 m/s for the auto regressive approach. The SNR was 75 dB. The next objective was to compare the estimated Doppler velocity obtained using the two algorithms with the experimental values recorded in real time. The setup consisted of a receiver hydrophone attached to a towing carriage that moved with a known velocity with respect to a stationary acoustic source. The source transmitted 375 kHz pilot tone. The received pilot tone data were preprocessed using the two algorithms to estimate both Doppler shift and Doppler velocity. The accuracy of the algorithms was compared against the true velocity values of the carriage. The RMSE for a message from experiments conducted indoor for constant velocity of 0.4 m/s was 0.6055 m/s using moving average, 0.0780 m/s using auto regressive approach. The SNIR was 6.3 dB. / by Poorani Kathiroli. / Thesis (M.S.C.S.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.

One of the greatest challenges to data mining is erroneous or noisy data. Several studies have noted the weak performance of classification models trained from low quality data. This dissertation shows that low quality data can also impact the effectiveness of feature selection, and considers the effect of class noise on various feature ranking techniques. It presents a novel approach to feature ranking based on ensemble learning and assesses these ensemble feature selection techniques in terms of their robustness to class noise. It presents a noise-based stability analysis that measures the degree of agreement between a feature ranking techniques output on a clean dataset versus its outputs on the same dataset but corrupted with different combinations of noise level and noise distribution. It then considers classification performances from models built with a subset of the original features obtained after applying feature ranking techniques on noisy data. It proposes the focused ensemble feature ranking as a noise-tolerant approach to feature selection and compares focused ensembles with general ensembles in terms of the ability of the selected features to withstand the impact of class noise when used to build classification models. Finally, it explores three approaches for addressing the combined problem of high dimensionality and class imbalance. Collectively, this research shows the importance of considering class noise when performing feature selection. / by Wilker Altidor. / Thesis (Ph.D.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.

Possibly the largest problem when working in bioinformatics is the large amount of data to sift through to find useful information. This thesis shows that the use of feature selection (a method of removing irrelevant and redundant information from the dataset) is a useful and even necessary technique to use in these large datasets. This thesis also presents a new method in comparing classes to each other through the use of their features. It also provides a thorough analysis of the use of various feature selection techniques and classifier in different scenarios from bioinformatics. Overall, this thesis shows the importance of the use of feature selection in bioinformatics. / by David Dittman. / Thesis (M.S.C.S.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.

Machine learning techniques allow useful insight to be distilled from the increasingly massive repositories of data being stored. As these data mining techniques can only learn patterns actually present in the data, it is important that the desired knowledge be faithfully and discernibly contained therein. Two common data quality issues that often affect important real life classification applications are class noise and class imbalance. Class noise, where dependent attribute values are recorded erroneously, misleads a classifier and reduces predictive performance. Class imbalance occurs when one class represents only a small portion of the examples in a dataset, and, in such cases, classifiers often display poor accuracy on the minority class. The reduction in classification performance becomes even worse when the two issues occur simultaneously. To address the magnified difficulty caused by this interaction, this dissertation performs thorough empirical investigations of several techniques for dealing with class noise and imbalanced data. Comprehensive experiments are performed to assess the effects of the classification techniques on classifier performance, as well as how the level of class imbalance, level of class noise, and distribution of class noise among the classes affects results. An empirical analysis of classifier based noise detection efficiency appears first. Subsequently, an intelligent data sampling technique, based on noise detection, is proposed and tested. Several hybrid classifier ensemble techniques for addressing class noise and imbalance are introduced. Finally, a detailed empirical investigation of classification filtering is performed to determine best practices. / by Amri Napolitano. / Thesis (Ph.D.)--Florida Atlantic University, 2009. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2009. Mode of access: World Wide Web.

by Lai Wai Kuen. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves [119-125]). / Abstract --- p.i / Acknowledgement --- p.ii / Table of Contents --- p.iii / Chapter Chapter 1 --- Introduction / Chapter 1.1 --- Background - The Need for Nonlinear Filtering --- p.1.1 / Chapter 1.2 --- Nonlinear Filtering --- p.1.2 / Chapter 1.3 --- Goal of the Work --- p.1.4 / Chapter 1.4 --- Organization of the Thesis --- p.1.5 / Chapter Chapter 2 --- An Overview of Robust Estimator Based Filters Morphological Filters / Chapter 2.1 --- Introduction --- p.2.1 / Chapter 2.2 --- Signal Representation by Sets --- p.2.2 / Chapter 2.3 --- Robust Estimator Based Filters --- p.2.4 / Chapter 2.3.1 --- Filters based on the L-estimators --- p.2.4 / Chapter 2.3.1.1 --- The Median Filter and its Derivations --- p.2.5 / Chapter 2.3.1.2 --- Rank Order Filters and Derivations --- p.2.9 / Chapter 2.3.2 --- Filters based on the M-estimators (M-Filters) --- p.2.11 / Chapter 2.3.3 --- Filter based on the R-estimators --- p.2.13 / Chapter 2.4 --- Filters based on Mathematical Morphology --- p.2.14 / Chapter 2.4.1 --- Basic Morphological Operators --- p.2.14 / Chapter 2.4.2 --- Morphological Filters --- p.2.18 / Chapter 2.5 --- Chapter Summary --- p.2.20 / Chapter Chapter 3 --- Multi-Structuring Element Erosion Filter / Chapter 3.1 --- Introduction --- p.3.1 / Chapter 3.2 --- Problem Formulation --- p.3.1 / Chapter 3.3 --- Description of Multi-Structuring Element Erosion Filter --- p.3.3 / Chapter 3.3.1 --- Definition of Structuring Element for Multi-Structuring Element Erosion Filter --- p.3.4 / Chapter 3.3.2 --- Binary multi-Structuring Element Erosion Filter --- p.3.9 / Chapter 3.3.3 --- Selective Threshold Decomposition --- p.3.10 / Chapter 3.3.4 --- Multilevel Multi-Structuring Element Erosion Filter --- p.3.15 / Chapter 3.3.5 --- A Combination of Multilevel Multi-Structuring Element Erosion Filter and its Dual --- p.3.21 / Chapter 3.4 --- Chapter Summary --- p.3.21 / Chapter Chapter 4 --- Properties of Multi-Structuring Element Erosion Filter / Chapter 4.1 --- Introduction --- p.4.1 / Chapter 4.2 --- Deterministic Properties --- p.4.2 / Chapter 4.2.1 --- Shape of Invariant Signal --- p.4.3 / Chapter 4.2.1.1 --- Binary Multi-Structuring Element Erosion Filter --- p.4.5 / Chapter 4.2.1.2 --- Multilevel Multi-Structuring Element Erosion Filter --- p.4.16 / Chapter 4.2.2 --- Rate of Convergence of Multi-Structuring Element Erosion Filter --- p.4.25 / Chapter 4.2.2.1 --- Convergent Rate of Binary Multi-Structuring Element Erosion Filter --- p.4.25 / Chapter 4.2.2.2 --- Convergent Rate of Multilevel Multi-Structuring Element Erosion Filter --- p.4.28 / Chapter 4.3 --- Statistical Properties --- p.4.30 / Chapter 4.3.1 --- Output Distribution of Multi-Structuring Element Erosion Filter --- p.4.30 / Chapter 4.3.1.1 --- One-Dimensional Statistical Analysis of Multilevel Multi-Structuring Element Erosion Filter --- p.4.31 / Chapter 4.3.1.2 --- Two-Dimensional Statistical Analysis of Multilevel Multi-Structuring Element Erosion Filter --- p.4.32 / Chapter 4.3.2 --- Discussions on Statistical Properties --- p.4.36 / Chapter 4.4 --- Chapter Summary --- p.4.40 / Chapter Chapter 5 --- Performance Evaluation / Chapter 5.1 --- Introduction --- p.5.1 / Chapter 5.2 --- Performance Criteria --- p.5.2 / Chapter 5.2.1 --- Noise Suppression --- p.5.5 / Chapter 5.2.2 --- Subjective Criterion --- p.5.16 / Chapter 5.2.3 --- Computational Requirement --- p.5.20 / Chapter 5.3 --- Chapter Summary --- p.5.23 / Chapter Chapter 6 --- Recapitulation and Suggestions for Further Work / Chapter 6.1 --- Recapitulation --- p.6.1 / Chapter 6.2 --- Suggestions for Further Work --- p.6.4 / Chapter 6.2.1 --- Probability Measure Function for the Two-Dimensional Filter --- p.6.4 / Chapter 6.2.2 --- Hardware Implementation --- p.6.5 / References / Appendices

Thesis. 1977. M.S.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / John C. Doyle. / M.S.

Thesis. 1978. Elec.E.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / Elec.E.

Thesis. 1977. M.S.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / M.S.

Thesis. 1976. Ph.D.--Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND AERONAUTICS. / Vita. / Bibliography : leaves 226-229. / Ph.D.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1979. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by Paul Moroney. / Ph.D.